Predicting the next application that you are going to use on aviate

ABSTRACT

In one embodiment, a current context of a mobile device may be ascertained, where the current context includes an indication of a last application opened via the mobile device, wherein the last application opened is one of a plurality of applications installed on the mobile device. A probability, for each of the plurality of applications, that a user of the mobile device will use the corresponding application under the current context may be determined, where the probability for at least a portion of the plurality of applications is determined by applying a computer-generated model to the current context. One or more of the plurality of the applications may be identified based, at least in part, upon the probability, for each one of the plurality of applications, that the user of the mobile device will use the corresponding application.

RELATED APPLICATIONS

This application is a continuation and claims priority of U.S. patentapplication Ser. No. 14/586,635, entitled “Predicting the NextApplication You are Going to Use on Aviate,” by Silvestri et al, filedon Dec. 30, 2014, which is incorporated herein by reference in itsentirety and for all purposes.

BACKGROUND OF THE INVENTION

The disclosed embodiments relate generally to computer-implementedmethods and apparatus for managing applications.

Mobile applications are becoming ubiquitous due to the increasingpopularity of mobile devices such as smartphones and tablets. Due to anincreasing availability and usage of mobile applications, mobile devicesoften have a very large number of applications installed. Given thelimited screen size of mobile devices, it is often tedious for users tosearch for an application they want to use through a potentially verylarge collection of installed applications.

To find applications that are used often, a user may manually organizetheir applications on their mobile device. For example, frequently usedapplications may be placed on a homescreen.

SUMMARY OF THE INVENTION

The disclosed embodiments provide a personalized application predictionsystem. In one embodiment, a current context of a mobile device may beascertained, where the current context includes an indication of a lastapplication opened via the mobile device, wherein the last applicationopened is one of a plurality of applications installed on the mobiledevice. A probability, for each of the plurality of applications, that auser of the mobile device will use the corresponding application underthe current context may be determined, where the probability for atleast a portion of the plurality of applications is determined byapplying a computer-generated model to the current context, wherein thecomputer-generated model is associated with the mobile device. One ormore of the plurality of the applications may be identified based, atleast in part, upon the probability, for each one of the plurality ofapplications, that the user of the mobile device will use thecorresponding application.

In another embodiment, it may be ascertained whether a threshold amountof contextual information pertaining to usage of at least a portion of aplurality of applications installed on a mobile device is available,where the contextual information includes a context pertaining to one ormore actions detected via the mobile device. A probability, for each ofthe plurality of applications, that a user of the mobile device will usethe corresponding application under a current context may be determined,based, at least in part, upon whether a threshold amount of contextualinformation pertaining to usage of at least a portion of the pluralityof applications installed on the mobile device is available. One or moreof the plurality of the applications may be identified based, at leastin part, upon the ascertained probability, for each one of the pluralityof applications, that the user of the mobile device will use thecorresponding application.

In another embodiment, the invention pertains to a device comprising aprocessor, memory, and a display. The processor and memory areconfigured to perform one or more of the above described methodoperations. In another embodiment, the invention pertains to a computerreadable storage medium having computer program instructions storedthereon that are arranged to perform one or more of the above describedmethod operations.

These and other features and advantages of the present invention will bepresented in more detail in the following specification of the inventionand the accompanying figures which illustrate by way of example theprinciples of the invention.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 is a diagram illustrating an example system in which embodimentsof the invention may be implemented.

FIG. 2 is a graphical user interface illustrating an example homescreenthat may be implemented in accordance with various embodiments.

FIG. 3 is a process flow diagram illustrating an example method ofimplementing a mapper for use in generating distributed representationsof actions in accordance with various embodiments.

FIG. 4 is a diagram illustrating an example algorithm for implementing aTAN structure learning mapper in accordance with various embodiments.

FIG. 5 is a diagram illustrating an example TAN parameter learningmapper in accordance with various embodiments.

FIG. 6 is a process flow diagram illustrating an example method ofperforming personalized application prediction in accordance withvarious embodiments.

FIG. 7 is a process flow diagram illustrating another example method ofperforming personalized application prediction in accordance withvarious embodiments.

FIG. 8 is a schematic diagram illustrating an example embodiment of anetwork in which various embodiments may be implemented.

FIG. 9 is a schematic diagram illustrating an example client device inwhich various embodiments may be implemented.

FIG. 10 is a schematic diagram illustrating an example computer systemin which various embodiments may be implemented.

DETAILED DESCRIPTION OF THE SPECIFIC EMBODIMENTS

Reference will now be made in detail to specific embodiments of thedisclosure. Examples of these embodiments are illustrated in theaccompanying drawings. While the disclosure will be described inconjunction with these specific embodiments, it will be understood thatit is not intended to limit the disclosure to these embodiments. On thecontrary, it is intended to cover alternatives, modifications, andequivalents as may be included within the spirit and scope of thedisclosure as defined by the appended claims. In the followingdescription, numerous specific details are set forth in order to providea thorough understanding of the disclosure. The disclosed embodimentsmay be practiced without some or all of these specific details. In otherinstances, well known process operations have not been described indetail in order not to unnecessarily obscure the disclosure. TheDetailed Description is not intended as an extensive or detaileddiscussion of known concepts, and as such, details that are knowngenerally to those of ordinary skill in the relevant art may have beenomitted or may be handled in summary fashion.

Subject matter will now be described more fully hereinafter withreference to the accompanying drawings, which form a part hereof, andwhich show, by way of illustration, specific example embodiments.Subject matter may, however, be embodied in a variety of different formsand, therefore, covered or claimed subject matter is intended to beconstrued as not being limited to any example embodiments set forthherein; example embodiments are provided merely to be illustrative.Likewise, a reasonably broad scope for claimed or covered subject matteris intended. Among other things, for example, subject matter may beembodied as methods, devices, components, or systems. Accordingly,embodiments may, for example, take the form of hardware, software,firmware or any combination thereof (other than software per se). Thefollowing detailed description is, therefore, not intended to be takenin a limiting sense.

Throughout the specification and claims, terms may have nuanced meaningssuggested or implied in context beyond an explicitly stated meaning.Likewise, the phrase “in one embodiment” as used herein does notnecessarily refer to the same embodiment and the phrase “in anotherembodiment” as used herein does not necessarily refer to a differentembodiment. It is intended, for example, that claimed subject matterinclude combinations of example embodiments in whole or in part.

In general, terminology may be understood at least in part from usage incontext. For example, terms, such as “and”, “or”, or “and/or,” as usedherein may include a variety of meanings that may depend at least inpart upon the context in which such terms are used. Typically, “or” ifused to associate a list, such as A, B or C, is intended to mean A, B,and C, here used in the inclusive sense, as well as A, B or C, here usedin the exclusive sense. In addition, the term “one or more” as usedherein, depending at least in part upon context, may be used to describeany feature, structure, or characteristic in a singular sense or may beused to describe combinations of features, structures or characteristicsin a plural sense. Similarly, terms, such as “a,” “an,” or “the,” again,may be understood to convey a singular usage or to convey a pluralusage, depending at least in part upon context. In addition, the term“based on” may be understood as not necessarily intended to convey anexclusive set of factors and may, instead, allow for existence ofadditional factors not necessarily expressly described, again, dependingat least in part on context.

Example System

FIG. 1 is a diagram illustrating an example system in which variousembodiments may be implemented. As shown in FIG. 1, the system mayinclude one or more servers 102, which may be associated with a web sitesuch as a social networking web site. Examples of social networking websites include Yahoo, Facebook, Tumblr, LinkedIn, Flickr, and Meme. Theserver(s) 102 may enable the web site to provide a variety of servicesto its users. More particularly, the server(s) 102 may include a webserver, search server, and/or content server.

A plurality of clients 106, 108, 110 may access a web service on a webserver via a network 104. The network 104 may take any suitable form,such as a wide area network or Internet and/or one or more local areanetworks (LAN's). The network 104 may include any suitable number andtype of devices, e.g., routers and switches, for forwarding search orweb object requests from each client to a search or web application andsearch or web results back to the requesting clients.

The disclosed embodiments may also be practiced in a wide variety ofnetwork environments (represented by network 104) including, forexample, TCP/IP-based networks, telecommunications networks, wirelessnetworks, etc. In addition, computer program instructions with whichembodiments of the invention may be implemented may be stored in anytype of computer-readable media, and may be executed according to avariety of computing models including a client/server model, apeer-to-peer model, on a stand-alone computing device, or according to adistributed computing model in which various of the functionalitiesdescribed herein may be effected or employed at different locations.

In accordance with various embodiments, the clients 106, 108, 110 mayinstall applications from server(s) via the network 104. In addition,the clients 106, 108, 110 may open or otherwise access applicationsinstalled on the clients 106, 108, 110. Each of the clients 106, 108,110 may include a mobile device. An example client device will bedescribed in further detail below.

As will be described in further detail below, a personalized applicationprediction system may leverage a context that is continuously sensed bya mobile device. More particularly, the context may indicate a sequenceof real-time actions that are sensed via the mobile device. Such actionsmay include those initiated by the user directly (e.g., by opening anapplication), as well as those that are initiated by the user indirectly(e.g., by driving or walking with the mobile device).

A personalized application prediction system may be implemented via anyof the clients 106, 108, 110 and/or remotely located server(s) 102. Aswill be described in further detail below, a list of applicationsinstalled on a mobile device may be identified. Given a current contextC, one or more of the applications that a user of the mobile device hasa high probability of using may be identified. This may be accomplishedby applying a prediction model to predict the application(s) that aremost likely to be used next or in the immediate future by a user of themobile device under a particular context C. In this manner, it ispossible to anticipate application(s) that a user of the mobile deviceis likely to use even before the user clicks on an icon of theapplication on his or her mobile device.

Once identified, at least one of the identified applications may belaunched and/or recommended to the user via the mobile device.Furthermore, an advertisement for at least one of the identifiedapplication(s) may be presented to the user.

A content server may store content for presentation to users. Forexample, a content server may store web pages available on the Internetor data gathered via the Internet. As another example, a content servermay be an “ad server” that stores online advertisements for presentationto users. “Ad serving” refers to methods used to place onlineadvertisements on websites, in applications, or other places where usersare more likely to see them, such as during an online session.

An advertisement may include text, one or more images, video, and/oraudio. An advertisement may also include one or more hypertext links,enabling a user to proceed with the purchase of a particular product orservice via the Internet.

Personalized application prediction may be performed based, at least inpart, upon a context associated with a particular mobile device, as willbe described in further detail below. More particularly, personalizedapplication prediction for a given mobile node may be performed using aprediction model associated with the mobile node. Where the context ofthe mobile device is unavailable or otherwise limited, personalizedapplication prediction may be performed based, at least in part, uponinformation associated with a set of other mobile devices that do notinclude the mobile device. Such information may include historical dataand/or information derived via a prediction model associated with atleast one of the other mobile devices. In some embodiments, personalizedapplication prediction may be performed using information from a userprofile of a user of the mobile device.

A variety of mechanisms may be implemented to support the generation ofuser profiles including, but not limited to, collecting or miningnavigation history, stored documents, tags, or annotations, to provide afew examples. Profiles of users of a search engine, for example, maygive a search engine provider a mechanism to retrieve annotations, tags,stored pages, navigation history, or the like, which may be useful formaking relevance determinations of search results, such as with respectto a particular user.

In accordance with various embodiments, the server(s) 102 may haveaccess to one or more user logs 118 (e.g., user databases) into whichuser information is retained for each of a plurality of users. This userinformation or a portion thereof may be referred to as a user profile.More particularly, the user profile may include public information thatis available in a public profile and/or private information. The userlogs 118 may be retained in one or more memories that are coupled to theserver 102.

The user information retained in the user logs 118 may indicate aplurality of features for each user. More particularly, the features mayinclude personal information such as demographic information (e.g., ageand/or gender) and/or geographic information (e.g., residence address,work address, zip code, and/or area code). In addition, each time a userperforms online activities such as clicking on a web page (or regionthereof) or an advertisement, or purchasing goods or services,information regarding such activity or activities may be retained asuser data in the user logs 118. For instance, the user data that isretained in the user logs 118 may indicate the identity of web sitesvisited, identity of ads that have been selected (e.g., clicked on)and/or a timestamp. In addition, the features may indicate a purchasehistory with respect to one or more products, one or more types ofproducts, one or more services, and/or one or more types of services.Additional features may indicate one or more interests of the userand/or applications that have been selected.

The user logs 118 may further include query logs into which searchinformation is retained. Each time a user performs a search on one ormore search terms, information regarding such search may be retained inthe query logs. For instance, the user's search request may contain anynumber of parameters, such as user or browser identity and the searchterms, which may be retained in the query logs. Additional informationrelated to the search, such as a timestamp, may also be retained in thequery logs along with the search request parameters. When results arepresented to the user based on the entered search terms, parameters fromsuch search results may also be retained in the query logs. For example,an identity of the specific search results (e.g., Uniform ResourceLocators (URLs)), such as the web sites, the order in which the searchresults are presented, whether each search result is a sponsored oralgorithmic search result, the owner (e.g., web site) of each searchresult, and/or whether each search result is selected (i.e., clicked on)by the user (if any), may be retained in the query logs.

In accordance with various embodiments, a user profile may be associatedwith one or more client devices, which may include one or more mobiledevices. Conversely, each client device may be associated with a set ofone or more users, enabling user profile(s) associated with the clientdevice to be identified.

Each mobile device may also have an associated device profile. Thedevice profile may include a current context, as will be described infurther detail below. The device profile may also identify allapplications installed on the mobile device. In addition, the deviceprofile may further include historical data that may be used to train anapplication prediction model. In some embodiments, the device profilemay further include parameter values associated with the predictionmodel.

A device profile of a mobile node may be stored locally on a mobiledevice and/or remotely at a remotely located server. In addition, adevice profile or information from a device profile may be transmittedor otherwise shared between a mobile device and the remotely locatedserver. Thus, the server may have access to a plurality of deviceprofiles associated with a plurality of mobile devices. Therefore, theserver may identify a class of mobile devices sharing a set ofcharacteristics with a given mobile device based upon theircorresponding device profiles and/or user profiles.

One of the major issues associated with the large number of applicationsinstalled on many mobile devices is that of their visibility. Moreparticularly, when a user wishes to use a particular application, it isnot always available immediately and the search through the large numberof installed applications might take a significant amount of time. As aresult, homescreen applications are increasing in popularity.

A homescreen application may act as an intelligent layer between anunderlying mobile operating system of a mobile device and a userinterface of the mobile device. The homescreen application may managethe installed apps in a highly personalized manner rather than simplyrelying on manual (or trivial) categorization schemes. In accordancewith one embodiment, the personalized application prediction system maybe implemented, at least in part, via a homescreen application of themobile device. A graphical user interface illustrating an examplehomescreen that may be implemented is shown in FIG. 2.

Embodiments disclosed herein may be implemented via remotely locatedserver(s) and/or the clients. For example, various features may beimplemented via a homescreen application installed on the clients. Thedisclosed embodiments may be implemented via software and/or hardware.

Application Prediction Problem

An application prediction problem may be formally defined as follows:

Given a list of applications {au1; au2; : : : ; aun} installed on amobile device of a user u and the user's context C, the problem ofapplication usage prediction is to find at least one application a_(ui)that has the largest probability of being used under the user's contextC. Specifically, we aim to solve the problem: Max a_(u1) P(a_(ui)|C, u),∀i, 1≤i≤n.

Features Used to Solve the Application Prediction Problem

Various features may be used to solve the application predictionproblem. Such features may include signal features that may be directlyobtained from sensors of a mobile device. In addition, the features mayinclude distributed features, which may indicate sequential patternswith respect to application usage. Examples of signal features anddistributed features will be described in further detail below.

Signal Features

The signal features may be obtained automatically by the mobile devicewithout initiation by a user. The signal features may define the user'sspatiotemporal context.

In accordance with various embodiments, the signal features may includetime, latitude, longitude, speed, Global Positioning System (GPS)accuracy, context trigger, context pulled, charge cable, and/or audiocable. The time feature may indicate a current time, whether the time isin the morning or evening, a day of the week, and/or a specific date. Inone embodiment, the time feature may be normalized between00:00:00-23:59:59 to capture daily application usage patterns. Thelatitude feature and longitude feature may specify the latitude andlongitude of the mobile device ascertained via a GPS system, while thespeed feature may indicate the speed with which the mobile device istraveling. The GPS accuracy feature may indicate an accuracy (or percenterror) of the latitude and longitude features.

The charge cable feature may indicate whether a charge cable is pluggedinto the mobile device. Similarly, the audio cable feature may indicatewhether an audio cable is plugged into the mobile device.

The context trigger feature may indicate a context of the user. Forexample, the context trigger feature may indicate that the user is atwork or at home. Similarly, the context pulled feature may indicatewhether the user has explicitly switched the context in which the useroperates. More particularly, the user may define a context or select oneof a plurality of contexts in which the user would like to operate. Bydefining a context, the user may add a new context to one of theplurality of contexts. As a result, the plurality of contexts that arepossible may be unique to a specific mobile device. Example contextsthat may be selectable by a user include home, work, morning, evening,“start of day,” “I'm with my kids,” and “I'm at the airport.”

One or more of the signal features may be used separately, or incombination with one another, to represent the user's context inrelation to usage by the user of applications installed on the user'smobile device. More particularly, one or more of the signal features maybe used to represent the user's context in relation to an applicationaction such as an application open event with respect to an applicationinstalled on the user's mobile device.

Distributed Features

One or more distributed features may capture contextual informationpertaining to (e.g., surrounding) application actions such asapplication open actions. Thus, the contextual information may pertainto usage of applications installed on a mobile device. Moreparticularly, a distributed feature may indicate a sequential pattern ofactions occurring prior to and/or after an application action. In oneembodiment, the application actions may include an application openaction, a location update action, a charge cable action, an audio cableaction, a context trigger action, and/or a context pulled action

A location update action may indicate that the location of the mobiledevice has been updated. For example, it may be ascertained that thelocation of the mobile device has been updated based, at least in part,upon GPS information such as latitude, longitude, and/or accuracyindicating that the location of the mobile device has changed.

Usage of an application may be ascertained via an application openaction that has been detected. More particularly, an application openaction may indicate an action of a user of the mobile device to open acorresponding application. An application open action with respect to afirst application (e.g., Application A) may be considered distinct froman application open action with respect to a second application (e.g.,Application B).

A record of application actions detected via a mobile device may bemaintained on the mobile device. For example, the record of theapplication actions may be maintained by a homescreen applicationinstalled on the mobile device. In addition, a record of the applicationactions detected via a mobile device may be maintained on a remotelylocated server. Thus, a remotely located server may maintain a record ofthe application actions for a plurality of mobile devices (e.g., intheir respective device profiles).

In accordance with various embodiments, a timestamp is recorded inassociation with each of the actions detected via a mobile device. As aresult, it is possible to identify a context of each of the recordedactions. The context of an action may be defined as a sequence ofactions that are conducted before and/or after the action within aparticular time period of the action.

A Gaussian based method may be applied to identify the context of arecorded action such as an application open action. More particularly,the context may be determined by sampling data within a time span suchthat a subset of data before and/or after the action is selected. Basedon the sampled data, it is possible to identify one or more otheractions that occur before and/or after the action.

For example, the context of App Open:com.android.dialer may bedetermined by sampling data within a time span from an empiricallydefined Gaussian distribution. Based on the sampled time span, we canregard the app actions App Open:com.skype.raider and LocationUpdate:(37.393093, −122.079788, 20.000000, 0.000000) as the context ofthe action App Open:com.android.dialer.

In accordance with various embodiments, the context of an action may berepresented by a corresponding distributed representation. Moreparticularly, distributed representations may be generated forpredicting actions surrounding an application action such as anapplication open action for a particular application. Distributedrepresentations for application actions may be generated usinghistorical data associated with the corresponding mobile device.

Distributed features may be extracted by considering sequentialrelationships among application actions. More particularly, adistributed representation associated with an application action mayindicate a sequential pattern of actions occurring prior to the actionwithin a particular time period and/or after the action within aparticular time period.

It is desirable to find distributed representations that are useful forpredicting occurrence of actions. More formally, given an action T suchas an application open action and its context C_(τ), the objective is tomaximize the average log probability:

${\frac{1}{T}{\sum\limits_{t = 1}^{T}{\sum\limits_{\tau^{\prime} \in C_{t}}^{\;}{\log \; {p\left( \tau^{\prime} \middle| \tau_{t} \right)}}}}},$

where C_(τ) is the context of τ and T is the number of distinct actions.The probability p(τ′|τ_(t)) is calculated as follows:

${p\left( \tau^{\prime} \middle| \tau_{t} \right)} = \frac{e^{V_{\tau^{\prime}}V_{\tau_{t}}}}{\sum\limits_{\tau}^{T}{e^{V_{\tau}}V_{\tau_{t}}}}$

where V_(τ) is the distributed representation of τ and V_(Tτ) is thedistributed representation of τ_(t).

In order to enhance the efficiency, the probability may be approximatedby performing a hierarchical softmax, as disclosed in Tomas Mikolov,Ilya Sutskever, Kai Chen, Greg S Corrado, and Jeff Dean, Distributedrepresentations of words and phrases and their compositionality,Advances in Neural Information Processing Systems, 2013, pp. 3111-3119,which is incorporated herein by reference. More particularly, thehierarchical softmax may implement a binary tree representation with theT actions as its leaves and, for each node, explicitly represent therelative probabilities of its child nodes. A random walk may beperformed that assigns probabilities to actions.

In order to efficiently compute the distributed representations of theactions, a MapReduce paradigm may be employed to parallelize theprocess. More particularly, a MapReduce procedure may return updatedfeature vectors that represent the distributed representations of theactions. An example MapReduce procedure will be described in furtherdetail below.

FIG. 3 is a process flow diagram illustrating an example method ofimplementing a mapper for use in generating distributed representationsof actions in accordance with various embodiments. As shown at 302, dataassociated with application actions that have been recorded for themobile device may be obtained. As described above, such data may berepresented in the form of vectors. A Huffman tree may be initializedbased upon the data (e.g., vectors) at 304. If there are moreapplication actions that remain to be processed at 306, the dataassociated with the next application action may be obtained at 308. Atemporal gap g1 from a Gaussian distribution G may be drawn at 310. Moreparticularly, the temporal gap g1 may correspond to a period of timeimmediately prior to the application action. In addition, a temporal gapg2 from the Gaussian distribution G may be drawn at 312. Moreparticularly, the temporal gap g2 may correspond to a period of timeimmediately after the application action. Actions within the temporalgap g1 and the temporal gap g2 may be identified as the context Ca forthe action at 314. A distributed representation of the action may begenerated based, at least in part, on the context Ca at 316. Forexample, the distributed representation may be generated in the form ofa vector. This process may be repeated at 206 until no further actionsremain to be processed.

Vectors corresponding to distributed representations of the actions maybe output at 218. More particularly, the vectors corresponding to thedistributed representations of the actions may be output in associationwith corresponding keys at 318. For example, a key may correspond to atype of the action (e.g., Application Open for a particular applicationinstalled on the mobile device, Location Update, Charge Cable, AudioCable, Context Trigger, Context Pulled) and a sequential pattern ofactions identified within the context Ca.

A reducer may calculate an average of the vectors (e.g., for each actiontype or subset thereof) and normalize the resulting vectors. The reducermay operate in parallel with the mapper described above.

The distributed representation of each action may be generated based, atleast in part, on the corresponding context Ca using an algorithm thathas been trained to identify sequential patterns in the data and/orascertain a distribution of the data. More particularly, by identifyingsequential patterns within the data and a distribution of the patternsover time, it is possible to assign a probability of occurrence to eachof the patterns with respect to a corresponding action. Such analgorithm may be trained using a first subset of data points and testedusing a second subset of the data points, where each of the data pointsincludes an action and corresponding context.

A distributed representation may identify a pattern of actionssurrounding an action, where the pattern of actions identifies one ormore actions occurring prior to the action within a particular period oftime and/or one or more actions occurring after the action within aparticular period of time. The pattern may indicate an order of theactions occurring prior to the action with respect to one another, anorder of the actions occurring after the action with respect to oneanother, and/or an order of the actions occurring both prior to theaction and after the action with respect to one another. In addition,the pattern may also indicate an amount of time that has lapsed betweenany of the actions occurring prior to the action, an amount of time thathas lapsed between any of the actions occurring after the action, and/oran amount of time that has lapsed between any of the actions occurringboth prior to the action and after the action with respect to oneanother. The distributed representation may include the action orotherwise be associated with the action. The distributed representationmay further indicate a probability, for that mobile device, ofoccurrence of the pattern of actions in relation to the action. Thedistributed representation may be represented via a vector or othersuitable data structure.

Distributed representations of one or more application actions may bedefined as the distributed portion of the context. The applicationactions may include an Application Open action for an applicationinstalled on a mobile device. In addition, the application actions mayfurther include at least one of a Location Update, Charge Cable, AudioCable, Context Trigger, and/or Context Pulled. In one embodiment,distributed representations of the following six application actions maybe defined as the distributed portion of the context: Last ApplicationOpened, Last Location Update, Last Charge Cable, Last Audio Cable, LastContext Trigger, Last Context Pulled.

The distributed representations for a mobile device may be generated bythe mobile device and/or remotely located server. The resultingdistributed representations may be transmitted between the mobile deviceand remotely located server. As a result, the distributedrepresentations may be stored at the mobile device and/or at a remotelylocated server. Therefore, a remotely located server may store thedistributed representations for a plurality of mobile devices (e.g., intheir respective device profiles).

A prediction model may be applied to distributed representationsassociated with one or more distributed features to identify one or moreapplications that are most likely to be used by a user of a mobiledevice. Mechanisms for generating a prediction model will be describedin further detail below.

Generating a Prediction Model Associated with a Mobile Device

A prediction model may be used to determine, under a current context,the application(s) that the user of a mobile device is most likely touse next. More particularly, a distributed portion of the currentcontext may include distributed representations of one or moreapplication actions. The application actions used to represent thedistributed portion of the context may include a Last ApplicationOpened. In addition, the application actions used to represent thedistributed portion of the current context may further include a LastLocation Update, Last Charge Cable, Last Audio Cable, Last ContextTrigger, and/or Last Context Pulled. In one embodiment, the applicationactions used to represent the distributed portion of the context mayinclude a Last Location Update, Last Charge Cable, Last Audio Cable,Last Context Trigger, a Last Context Pulled, and a Last ApplicationOpened. Accordingly, the distributed representations of the applicationactions may be used as the features for application prediction.

In accordance with various embodiments, a prediction model for a mobiledevice may be generated using data that identifies actions and includesassociated distributed representations, where each of the distributedrepresentations corresponds to one of the actions. More particularly,the prediction model may be trained using a first subset of the data andtested using a second subset of the data to ensure that the trainedprediction model accurately predicts the application(s) that have a highprobability of being used (e.g., launched) next by a user of the mobiledevice under a given context. The prediction model may be trained toidentify patterns in the data before occurrence of the corresponding theactions and/or after occurrence of the corresponding actions, enablingthe prediction model to predict the application(s) that are likely to beused next by a user of the mobile device under a given context.

Training data used to train the prediction model may include distributedrepresentations associated with a single mobile device, which may haveone or more associated users. In some instances, the training data usedto train the prediction model may include the distributedrepresentations associated with a plurality of mobile devices. Each oneof the plurality of mobile devices may be associated with acorresponding set of one or more users. The plurality of mobile devicesmay have applications installed thereon that include the same pluralityof applications. In addition, the plurality of mobile devices may beassociated with users that share a set of the same characteristics suchas demographic characteristics.

The distributed representations associated with a given mobile devicemay be collected over a period of time. A distributed representation mayinclude a feature vector having values corresponding to a plurality offeatures. For example, the plurality of features may include a pluralityof actions such as those described herein.

A prediction model may be generated by a mobile device and/or remotelylocated server. For example, a remotely located server may generate andstore a plurality of prediction models, where each of the predictionmodels is associated with a corresponding one of a plurality of mobiledevices. The data used to generate the prediction model for a mobiledevice may be stored locally at the mobile device and/or remotely at astorage device or remotely located server.

Once generated, the prediction model may be applied to the currentcontext including the distributed representation to generate aprobability associated with each of a plurality of applicationsinstalled on the mobile device. The plurality of applications may beranked based, at least in part, upon the corresponding probabilities.One or more of the plurality of applications may be selected based, atleast in part, upon the corresponding probabilities. In this manner, theprediction model may identify one or more of the plurality ofapplications installed on the mobile device as having a highestprobability of being used next by the user of the mobile device.

In one embodiment, the prediction model may generate a matrix for theplurality of applications, where the matrix includes probabilitiesassociated with each one of a plurality of possible distributedrepresentations (which may correspond to all possible patterns ofdistributed representations) with respect to each of the plurality ofapplications. More particularly, each one of a plurality of rows (orcolumns) in the matrix may correspond to a corresponding one of theplurality of distributed representations, where the row (or column)includes a plurality of probabilities, where each one of the pluralityof probabilities may indicate a probability that the user of the mobiledevice will next use a corresponding one of the plurality ofapplications. Conversely, each one of a plurality of columns (or rows)in the matrix may correspond to an application profile for acorresponding one of the plurality of applications, where theapplication profile indicates a plurality of probabilities, where eachprobability indicates a likelihood that a user of the mobile device willselect the application from a corresponding one of a plurality ofcontexts (e.g., distributed representations). Thus, a row (or column) inthe matrix corresponding to the distributed representation of thecurrent context may be identified and probabilities associated with theapplications installed on the mobile device may be ascertained. Theplurality of applications may be ranked according to the correspondingprobabilities and one or more of the plurality of applications having ahighest probability may be identified.

The identified application(s) may be recommended, launched, or otherwisepresented to the user of the mobile device. In one embodiment, anapplication that is determined to have a highest probability of beingused next by the user of the mobile device may be launched such thatcontent is presented to the user of the mobile device. For example, whena user picks up the mobile device and unlocks it, a homescreenapplication may open and present the application. In another embodiment,the application that is determined to have the highest probability ofbeing used next by the user of the mobile device may be launched in thebackground as a silent process with an indication to the user that theapplication is available. In addition, a web page may be pre-loaded withcontent generated via the application.

The next application used by the user of the mobile device may beascertained. The prediction model may be updated based, at least inpart, upon the next application that is used by the user of the mobiledevice.

Implementing a Prediction Model Using a Bayesian Network

In accordance with various embodiments, a prediction model may beimplemented using a Bayesian Network. More particularly, the BayesianNetwork may be a parallel tree augmented naive Bayesian Network (PTAN).The prediction model may be deployed on parallel computing platformssuch as Hadoop. PTAN is a parallel version of the Tree Augmented NaïveBayes (TAN), which has been proposed in order to remove the strongassumptions of independence in native Bayes and find correlations amongattributes that are warranted by the training data. A discussion ofBayesian network classifiers can be found in Nir Friedman, Dan Geiger,and Moises Goldszmidt, Bayesian network classifiers, Machine learning 29(1997), no. 2-3, 131-163, which is incorporated herein by reference forall purposes. The training data may correspond to a single mobile deviceor a plurality of mobile devices.

The training procedure of PTAN can be divided into two phases: (1)parallel structure training; and (2) parallel parameter estimation.

Constructing the Bayesian Network

The first phase is to learn the structure of the Bayesian network. Theprocedure of constructing the PTAN Bayesian network from the trainingdata may be performed as follows:

1. Based on the training data including a plurality of distributedrepresentations (e.g., from a plurality of users), conditional mutualinformation between attributes may be computed for each one of aplurality of applications. The function of the conditional mutualinformation may be defined as follows:

${I_{p}\left( {x,\left. y \middle| a \right.} \right)} = {\sum\limits_{x,y,a}{{P\left( {x,y,a} \right)}\log {\frac{P\left( {x,\left. y \middle| a \right.} \right)}{{P\left( x \middle| a \right)}{P\left( y \middle| a \right)}}.}}}$

where x and y may designate user attributes, a designates a particularapplication, and the training data corresponds to a plurality of mobiledevices.

2. A complete undirected graph may be built in which the verticescorrespond to the features, where a set of features corresponds to adistributed representation with respect to an application. A weight ofan edge connecting feature f_(i) to f_(j) by I_(p)(f_(i), f_(j)|a) maybe annotated such that the weight indicates a probability of occurrence.

3. A maximum weighted spanning tree may be built for the completeundirected graph. The maximum spanning tree problem can be transformedto a minimum spanning tree problem simply by negating edge weights, asdiscussed in David Eppstein, Spanning trees and spanners, Handbook ofcomputational geometry (1999), 425-461, which is incorporated herein byreference for all purposes. Kruskal's algorithm may be used to solve theminimum spanning tree problem, as described in John C Gower and G J SRoss, Minimum spanning trees and single linkage cluster analysis,Applied statistics (1969), 54-64, which is incorporated herein byreference for all purposes.

4. The resulting undirected spanning tree may be transformed to adirected spanning tree by choosing a root variable and setting thedirection of all edges to be outward from it. A TAN model may beconstructed by adding a vertex labeled by adding an application variablea and adding an arc from a to each feature fi, where each feature ficorresponds to an action of a distributed representation.

The highest computational cost is associated with the first and thethird step in the above algorithm. If we assume there exist N traininginstances and each instance has n features then the first step hascomplexity of O(n²N). The third step has a complexity of O(n² log n).N>>log n, step 1 is the bottleneck of building the Bayesian networkstructure. Hence, in PTAN, we can parallelize step 1 using MapReduce.More particularly, a Mapper may be implemented for parallelizing step 1,while a Reducer can be straightforwardly developed by aggregating thevalues based on keys, which may correspond to application-distributedrepresentation combinations. An example algorithm for implementing a TANstructure learning mapper is shown in FIG. 4, while an example TANparameter learning mapper is shown in FIG. 5.

After the PTAN structure is obtained, we can estimate the conditionalprobability for each individual user. The conditional probability may beestimated as follows:

${{P\left( {f_{i} = {\left. k \middle| {{pa}\left( f_{i} \right)} \right. = j}} \right)} = \frac{N_{ijk} + N_{ijk}^{\prime}}{N_{ij} + N_{ij}^{\prime}}},$

where fi is the feature, pa(f_(i)) is the set of the parents of f_(i)and N′_(ijk) is a smoothing parameter that can be set, which may be 0.5by default. The smoothing operation may enhance the predictionperformance.

When a user has very few training instances for an application, theestimate of the conditional probability is unreliable. However, thisproblem is not present in the case of a naive Bayesian classifier wherethe training data corresponds to a plurality of mobile devices, sincethe Bayesian classifier may partition the data according to one or moreclass variables. Generally, all possible values of the class variablesare adequately represented in training data associated with a pluralityof mobile devices. Therefore, the estimated conditional probabilitiesare unlikely to be unreliable for a given class of users.

We can compute the probability that a user u is going to use mobile appaui when the user finds him/herself within the context C={f₁; f₂; : : :; f_(n)} as follows:

${{P\left( {\left. a_{ui} \middle| f_{1} \right.,f_{2},\ldots \;,f_{n}} \right)} \propto {{P\left( a_{ui} \right)}{P\left( {f_{1},f_{2},\ldots \;,\left. f_{n} \middle| a_{ui} \right.} \right)}}} = {{P\left( a_{ui} \right)}{\prod\limits_{i = 1}^{n}\; {P\left( f_{i} \middle| {{pa}\left( f_{i} \right)} \right)}}}$

where f_(i) represents the feature vectors.

One or more applications that have the highest probability may beidentified as the prediction result. The identified applications may berecommended or launched, as described above.

Cold Start Situations

Two of the main challenges we face when applications are deployed inreal-life environments include the Application Cold Start and the UserCold Start. The Application Cold Start may occur when a user installs anew application on his or her mobile device. Where the user has not yetopened the new application to use the application, contextualinformation pertaining to the new application will be unavailable.Alternatively, the user may have opened the new application a limitednumber of times. As a result, a minimum threshold amount of contextualinformation may be unavailable for use in training a prediction model.The User Cold Start may occur when contextual information pertaining toapplications installed on the mobile device is not available. This mayoccur after a user installs a homescreen application and opens thehomescreen application for the first time. Example methods of handlingthese two cold start problems will be described in further detail below.

Application Cold Start

For a newly installed application a, on a particular mobile device,contextual information pertaining to the application may be unavailableor limited for the mobile device. Thus, an adequate amount ofuser-specific information may be unavailable for the application. As aresult, the probability of opening the application on the mobile devicecannot be ascertained solely based on the contextual information.

In accordance with various embodiments, the probability of opening theapplication on the mobile device may be determined based, at least inpart, upon information associated with other mobile devices on which theapplication is installed, as will be described in further detail below.In this manner, the probability that a user of the mobile device willuse a newly installed application under a given context C may beestimated.

Generally, a fraction of the newly installed applications on a mobiledevice will be used a larger number of times and more frequently in thefirst several hours or days after installation of those applications ona mobile device, and then are used significantly fewer times and lessfrequently after the initial excitement of the newly installedapplications dissipate.

In contrast, some newly installed applications are used frequently for along-term period of time after their installation on a mobile device. Inorder to better estimate the probability that a user of the mobiledevice will next use a newly installed application under a given contextC, the newly installed applications may be categorized.

In one embodiment, each of the newly installed applications may becategorized as a short-term application or a long-term application. Moreparticularly, computer-readable instructions implementing Betadistribution may be used to model the temporal prominence of each newlyinstalled application. In order to differentiate applications by theirtemporal significance, excess kurtosis can be utilized to evaluate thetemporal peakedness of each application, as disclosed in GaminiPremaratne and Anil Bera, Modeling asymmetry and excess kurtosis instock return data, Illinois Research & Reference Working Paper No.00-123 (2000). The excess kurtosis ρ percentage of a Beta distributionBeta(α, β) may be defined as follows:

$\varrho = {\frac{6\left\lbrack {\alpha^{3} - {\alpha^{2}\left( {{a\; \beta} - 1} \right)} + {\beta^{2}\left( {\beta + 1} \right)} - {2\alpha \; {\beta \left( {\beta + 2} \right)}}} \right\rbrack}{{{\alpha\beta}\left( {\alpha + \beta + 2} \right)}\left( {\alpha + \beta + 3} \right)}.}$

An application with a high percentage value indicates that it is likelyto be a short-term application, while an application with a lowpercentage value is likely to be a long-term application. In thismanner, we can categorize the applications into short-term applicationsand long-term applications. For example, communication applications areusually long-term applications, while game applications tend to haveshort longevity.

Short-Term Application

Given the observations made above, in the case of applications that areclassified as short-term, the probability that a user of the mobiledevice will use a short-term application may be assigned a probabilitydetermined based, at least in part, upon information associated with aplurality of other mobile devices. For example, the probability may bedetermined based, at least in part, upon the average opening frequencyof the short-term application by a plurality of other mobile devices.The average opening frequency may be obtained from historical dataobtained from profiles associated with the plurality of other mobiledevices. Such mobile devices may be associated with a class of mobiledevices, as described herein.

After a period of time such as a fixed time period (e.g., severalhours), the probability determined based upon information associatedwith the other mobile devices may be replaced with a new probabilitydetermined based upon actual usage of the application by a user of themobile device.

Long-Term Application

For long-term applications, the concept of “wisdom of the crowd” may beexploited. When an application a, has received many openings on a mobiledevice, that data may be considered more “reliable”. However, when anapplication has received very few openings on the mobile device, theprobability that a user of the mobile device will open the applicationP(a_(i)) may be determined based, at least in part, upon informationassociated with a plurality of other mobile devices. For example, theinformation may indicate the average probability of opening theapplication a, on the plurality of other mobile devices.

In addition, the probability that a user of the mobile device will openthe application P(a_(i)) may be determined based, at least in part, uponinformation associated with the mobile device. For example, theinformation may indicate the number of openings of the application a_(i)on the mobile device and/or the number of openings of all applicationsinstalled on the mobile device. More particularly, the probability thata user of the mobile device will open the application P(a_(i)) may bedetermined based, at least in part, on the average probability ofopening the application a_(i) on a plurality of other mobile devices,the number of openings of the application a_(i) on the mobile device,and the number of openings of all applications installed on the mobiledevice.

In one embodiment, a Bayesian average [1] may be used to effectivelyblend actual usage information pertaining to the application for a userof the mobile device with information pertaining to the application forusers u of other mobile devices having the application installedthereon. The Bayesian average may be calculated as disclosed in George EP Box and George C Tiao, Bayesian inference in statistical analysis,vol. 40, John Wiley & Sons, 2011, which is incorporated by reference forall purposes.

In one embodiment, an equation for calculating the estimated probabilityof use of the application by a user of the mobile device may beapproximated as follows:

${P\left( a_{ui} \right)} = \frac{{C \cdot m_{a_{ui}}} + O_{a_{i}}}{C + O}$

where Oa_(i) is the number of openings of the application a, on themobile device, Oa_(i) is the sum of all application openings on themobile device, and ma_(i) is the average probability of opening theapplication a_(i) on a plurality of other mobile devices. C mayrepresent a constant whose value is set to match the expected variationbetween data sets containing the historical data associated withdifferent mobile devices.

The value of the estimated probability P for an application with arelatively small number of open events tends to be closer to the averageopening probability of that same application on all other mobiledevices. The more openings an application has received, the moreaccurate the probability estimation is. On the other hand, when anapplication has received just a relatively small number of openings, itsestimated probability value is close to the application's unweightedaverage opening probability computed on the plurality of other mobiledevices.

User Cold Start

In some instances, data pertaining to application usage of applicationsinstalled on a given mobile device may be unavailable. This may occur,for example, when the user purchases a new mobile device or the user hasrecently installed a homescreen application. In such instances, aprediction model associated with another set of one or more mobiledevices/users may be used and/or adapted. The prediction model may begenerated based upon historical data associated with one or more othermobile devices (or corresponding users) that are separate from themobile device. More particularly, the users of the other mobile devicesmay share a set of characteristics with the user of the mobile device,such as demographic characterisics. In addition, the other mobiledevices may all have applications installed thereon that include theplurality of applications that are installed on the mobile device (or asignificant portion of the plurality of applications).

Two different strategies to attack the user cold start problem will bediscussed below. These two different strategies are the Most SimilarUser Strategy and the Pseudo User Strategy.

Most Similar User Strategy

“The most similar user” may be identified based, at least in part, byidentifying at least one user of at least a subset of other mobiledevices based, at least upon characteristics of users of the othermobile devices (and characteristics of the user of the mobile device). Asimilarity between the user of the mobile device and users of theidentified other mobile devices may be ascertained by using metrics suchas, for instance, Jaccard similarity between the user of the mobiledevice and users of the other mobile devices.

After the most similar user has been found we can use thecomputer-generated model associated with the most similar user's mobiledevice to predict the behavior of the user of the mobile device. Thisstrategy, though, has a very important drawback. The user that has beenfound to be the most similar may have an application inventory that isvery different from the applications installed on the mobile device ofthe user. In some extreme cases the application inventory of the mobiledevice of the user may be totally different from the applicationsinstalled on mobile devices of other “known” users. As a result, themost similar user strategy would not have any advantage over a purelyrandom strategy. Of course, it is possible to place a threshold on theminimum acceptable similarity between the user of the mobile device andthe “most similar user.” However, while such a threshold would improvethe average accuracy of the method, it would also limit the coverage,i.e., the number of users/mobile devices for which probabilities can begenerated.

When a user installs a homescreen application such as Aviate on his orher mobile device, it is not true that we do not know anything about theuser. For example, it is possible to identify the applications that areinstalled on the user's mobile device. Thus, it may be desirable toidentify the “most similar user” at least in part by identifying othermobile devices that also have applications installed thereon thatinclude the applications that are installed on the user's mobile device(or at least a significant portion of these applications).

In one embodiment, the “most similar user” may be identified from usersof other mobile devices having applications installed thereon thatinclude the applications installed on the mobile device (or asignificant portion of the plurality of applications). Acomputer-generated model associated with the other mobile device of this“most similar user” may then be applied to estimate a probability of useof one of the applications installed on the mobile device by the user ofthe mobile device.

Pseudo User Strategy

In order to solve the problem of the strategy presented above, thePseudo User Strategy may be applied. The Pseudo User Strategy mayinclude obtaining historical data associated with one or more othermobile devices, which may in turn be used to train a computer-generatedmodel (e.g., PTAN model) for predicting behavior of the user of themobile device. Such historical data may be referred to as a “pseudohistory.”

Let us assume there are k users U={u₁, u₂, u₃, . . . , u_(k)}, and letus further assume that each user u has an application inventory I_(u).When a new user with application inventory I_(new) installs a homescreenapplication, we can find a subset of users U′ satisfying the followingconditions:

${\min {\sum\limits_{u \in U^{\prime}}{{c(u)}}}},{{s.t.{\bigcup_{u \in U^{\prime}}I_{u}}} = I_{new}}$

The cost of a user c(u) may be defined as the inverse of its similarityto the current user, i.e., c(u)=1/J(I_(new), I_(u)). The logic behindthe problem formation is that we want to find a small number of similarusers whose application inventories cover all of the application in theinventory of the user of the mobile device. It is straightforward toprove that the problem is NP-hard (it is essentially a weightedset-covering problem) and an example of the greedy algorithm that may beused to generate the pseudo user is presented below, which has anapproximation ratio of O(log |U|).

1:  U ← ⌀ 2:  I ← ⌀ 3:  repeat$4:\mspace{11mu} {{{choose}\mspace{14mu} u} \in {\mspace{14mu} {minimizing}\mspace{14mu} \frac{c(u)}{{I\bigcup{I_{u} - I}}}}}$5:  let  U ← U⋃u 6:  let  I ← I⋃I_(u) 7:  until  I = I_(new)

Example Methods of Implementing Personalized Application Prediction

FIG. 6 is a process flow diagram illustrating an example method ofperforming personalized application prediction in accordance withvarious embodiments. A current context of a mobile device may beascertained at 602, where the current context includes an indication ofa last application opened via the mobile device, wherein the lastapplication opened is one of a plurality of applications installed onthe mobile device. The current context may further indicate a valueassociated with one or more of a Last Location Update, Last ChargeCable, Last Audio Cable, Last Context Trigger, or a Last Context Pulled.

A probability may be determined at 604, for each of the plurality ofapplications, that a user of the mobile device will use thecorresponding application under the current context. The probability forat least a portion of the plurality of applications may be determined byapplying a computer-generated model associated with the mobile device tothe current context. However, contextual information may not beavailable (or alternatively, may be limited) for some or all of theapplications installed on the mobile device. For example, contextualinformation associated with newly installed applications may beunavailable or limited. The probability associated with these newlyinstalled applications may be determined using another mechanism, asdescribed herein.

The computer-generated model may be trained using data associated withthe mobile device. More particularly, contextual information may becollected via the mobile device and the computer-generated modelassociated with the mobile device may be trained using at least aportion of the collected contextual information. The contextualinformation may include a context pertaining to one or more actionsdetected via the mobile device. The context may indicate a sequentialpattern of actions detected by the mobile device prior to one of the oneor more actions and/or after the one of the one or more actions. Atleast one of the actions may pertain to usage of at least a portion ofthe plurality of applications installed on the mobile device. Forexample, the actions may include an application open action. Inaddition, the actions may further include a location update action, acharge cable action, an audio cable action, a context trigger action,and/or or a context pulled action.

As described above, it may be determined whether any of the plurality ofapplications installed on the mobile device is a newly installedapplication. In some instances, it may be determined that a particularone of the plurality of applications is a newly installed applicationsuch that contextual information pertaining to usage of the newlyinstalled application on the mobile device cannot be ascertained (or maybe limited). Such a scenario may also be referred to as an applicationcold start. In an application cold start situation, it may be furtherdetermined whether the newly installed application is a short-termapplication or a long-term application. The probability that a user ofthe mobile device will use the newly installed application may beascertained based, at least in part, upon whether the newly installedapplication is a short-term application or a long-term application.

Upon determining that the particular application, which is a newlyinstalled application, is a short-term application, the probability thata user of the mobile device will use the newly installed application maybe ascertained based, at least in part, upon information associated withother mobile devices, such as those associated with a class of mobiledevices. For example, the class of mobile devices may include othermobile devices having applications installed thereon that include theplurality of applications. The class of mobile devices may also beassociated with users sharing a set of characteristics such asdemographic characteristics. In one embodiment, the information mayinclude historical data pertaining to a plurality of other mobiledevices with respect to the application. For example, the historicaldata may indicate a frequency with which the application is opened bythe plurality of other mobile devices. In this manner, the probabilitythat a user of the mobile device will use the short-term application maybe estimated. After a period of time such as a fixed time period (e.g.,several hours), the probability determined based upon informationassociated with the other mobile devices may be replaced with a newprobability determined based upon actual usage of the application by auser of the mobile device.

Upon determining that the particular application, which is a newlyinstalled application, is a long-term application, the probability thata user of the mobile device will use the newly installed application maybe ascertained based, at least in part, upon information associated withother mobile devices, such as those associated with a class of mobiledevices. For example, the class of mobile devices may include othermobile devices having applications installed thereon that include theplurality of applications. The class of mobile devices may also beassociated with users sharing a set of characteristics such asdemographic characteristics. In one embodiment, the information mayinclude historical data associated with a plurality of other mobiledevices with respect to the newly installed application. For example,the historical data may indicate an average opening probability of theparticular application on the other mobile devices. The historical datamay also include that pertaining to the plurality of applicationsinstalled on the mobile device. For example, the historical data mayindicate a number of openings of the newly installed application on themobile device and/or a number of openings of the plurality ofapplications on the mobile device.

One or more of the plurality of the applications may be identified at606 based, at least in part, upon the probability, for each one of theplurality of applications, that the user of the mobile device will usethe corresponding application. Once identified, the identifiedapplication(s) may be recommended or launched via the mobile device. Inaddition, an advertisement pertaining to the identified application(s)may be provided via the mobile device.

FIG. 7 is a process flow diagram illustrating another example method ofperforming personalized application prediction in accordance withvarious embodiments. It may be ascertained at 702 whether a thresholdamount of contextual information pertaining to usage of at least aportion of a plurality of applications installed on a mobile device isavailable, where the contextual information includes a contextpertaining to one or more actions. For example, contextual informationfor a newly installed application may be unavailable or limited. Asanother example, where the user is a new user (e.g., a homescreenapplication has recently been installed), contextual information may beunavailable or limited for one or more of the plurality of applications.

The context may include an indication of a sequential pattern of actionsdetected by the mobile device prior to one of the one or more actionsand/or after the one of the one or more actions. At least one of theactions may pertain to usage of at least a portion of the plurality ofapplications installed on the mobile device. For example, the actionsmay include an application open action. In addition, the actions mayfurther include a location update action, a charge cable action, anaudio cable action, a context trigger action, and/or or a context pulledaction.

For each of the plurality of applications installed on the mobiledevice, a probability may be determined at 704 that a user of the mobiledevice will use the corresponding application under a current contextbased, at least in part, upon whether a threshold amount of contextualinformation pertaining to usage of at least a portion of the pluralityof applications installed on the mobile device is available.

It may be ascertained that contextual information pertaining to usage ofat least a portion of the plurality of applications installed on themobile device is available. For an application for which contextualinformation is available (e.g., the user has previously opened theapplication), the probability that a user of the mobile device will usethat application may be determined by applying a computer-generatedmodel associated with the mobile device to the contextual information.

Alternatively, it may be ascertained that contextual informationpertaining to usage of at least a portion of the plurality ofapplications installed on the mobile device is not available. Such ascenario may be referred to as a user cold start. Thus, the probability,for each of the plurality of applications, that a user of the mobiledevice will use the corresponding application, may be determined based,at least in part, upon information associated with one or more othermobile devices, such as those associated with a class of mobile devices.In one embodiment, the information may be obtained via acomputer-generated model associated with the other mobile devices. Thecomputer-generated model may subsequently be updated or replaced basedupon actions recorded via the mobile device. In another embodiment, theinformation may include historical data, as described above. The classof mobile devices may include other mobile devices having applicationsinstalled thereon that include the plurality of applications. The classof mobile devices may also be associated with users sharing a set ofcharacteristics such as demographic characteristics with a user of themobile device.

In other scenarios, it may be ascertained that contextual informationpertaining to usage of an application does not exist or is limited, butit does exist for other application(s). Such a scenario may be referredto as an application cold start. In an application cold start situation,it may be determined whether the application (e.g., newly installedapplication) is a short-term application or a long-term application. Inan application cold start situation, it may be further determinedwhether the newly installed application is a short-term application or along-term application. The probability that a user of the mobile devicewill use the newly installed application may be ascertained based, atleast in part, upon whether the newly installed application is ashort-term application or a long-term application.

As described above, the probability that a user of the mobile devicewill use a newly installed application may be ascertained based, atleast in part, upon information associated with a plurality of othermobile devices, such as those associated with a class of mobile devices.For example, the class of mobile devices may include other mobiledevices having applications installed thereon that include the pluralityof applications. The class of mobile devices may also be associated withusers sharing a set of characteristics such as demographiccharacteristics with a user of the mobile node.

Where a newly installed application is determined to be a short-termapplication, the information may include historical data pertaining to aplurality of other mobile devices with respect to the application. Forexample, the historical data may indicate a frequency with which theapplication is opened by the plurality of other mobile devices. In thismanner, the probability that a user of the mobile device will use theshort-term application may be estimated. After a period of time such asa fixed time period (e.g., several hours), the probability determinedbased upon information associated with the other mobile devices may bereplaced with a new probability determined based upon actual usage ofthe application by a user of the mobile device.

Alternatively, where the newly installed application is determined to bea long-term application, the information may include historical datapertaining to a plurality of other mobile devices with respect to theapplication. For example, the historical data may indicate an averageopening probability of the particular application on the other mobiledevices. The information may also include historical data pertaining tousage of the plurality of applications installed on the mobile device.For example, the historical data may indicate a number of openings ofthe newly installed application on the mobile device and/or a number ofopenings of the plurality of applications on the mobile device.

One or more of the plurality of the applications may be identified at706 based, at least in part, upon the ascertained probability, for eachone of the plurality of applications, that the user of the mobile devicewill use the corresponding application. Once identified, the identifiedapplication(s) may be recommended or launched via the mobile device. Inaddition, an advertisement pertaining to the identified application(s)may be provided via the mobile device.

Network

A network may couple devices so that communications may be exchanged,such as between a server and a client device or other types of devices,including between wireless devices coupled via a wireless network, forexample. A network may also include mass storage, such as networkattached storage (NAS), a storage area network (SAN), or other forms ofcomputer or machine readable media, for example. A network may includethe Internet, one or more local area networks (LANs), one or more widearea networks (WANs), wire-line type connections, wireless typeconnections, or any combination thereof. Likewise, sub-networks, such asmay employ differing architectures or may be compliant or compatiblewith differing protocols, may interoperate within a larger network.Various types of devices may, for example, be made available to providean interoperable capability for differing architectures or protocols. Asone illustrative example, a router may provide a link between otherwiseseparate and independent LANs.

A communication link or channel may include, for example, analogtelephone lines, such as a twisted wire pair, a coaxial cable, full orfractional digital lines including T1, T2, T3, or T4 type lines,Integrated Services Digital Networks (ISDNs), Digital Subscriber Lines(DSLs), wireless links including satellite links, or other communicationlinks or channels, such as may be known to those skilled in the art.Furthermore, a computing device or other related electronic devices maybe remotely coupled to a network, such as via a telephone line or link,for example.

Content Distribution Network

A distributed system may include a content distribution network. A“content delivery network” or “content distribution network” (CDN)generally refers to a distributed content delivery system that comprisesa collection of computers or computing devices linked by a network ornetworks. A CDN may employ software, systems, protocols or techniques tofacilitate various services, such as storage, caching, communication ofcontent, or streaming media or applications. Services may also make useof ancillary technologies including, but not limited to, “cloudcomputing,” distributed storage, DNS request handling, provisioning,signal monitoring and reporting, content targeting, personalization, orbusiness intelligence. A CDN may also enable an entity to operate ormanage another's site infrastructure, in whole or in part.

Peer-to-Peer Network

A peer-to-peer (or P2P) network may employ computing power or bandwidthof network participants in contrast with a network that may employdedicated devices, such as dedicated servers, for example; however, somenetworks may employ both as well as other approaches. A P2P network maytypically be used for coupling devices via an ad hoc arrangement orconfiguration. A peer-to-peer network may employ some devices capable ofoperating as both a “client” and a “server.”

Wireless Network

A wireless network may couple client devices with a network. A wirelessnetwork may employ stand-alone ad-hoc networks, mesh networks, WirelessLAN (WLAN) networks, cellular networks, or the like.

A wireless network may further include a system of terminals, gateways,routers, or the like coupled by wireless radio links, or the like, whichmay move freely, randomly or organize themselves arbitrarily, such thatnetwork topology may change, at times even rapidly. A wireless networkmay further employ a plurality of network access technologies, includingLong Term Evolution (LTE), WLAN, Wireless Router (WR) mesh, or 2nd, 3rd,or 4th generation (2G, 3G, or 4G) cellular technology, or the like.Network access technologies may enable wide area coverage for devices,such as client devices with varying degrees of mobility, for example.

For example, a network may enable RF or wireless type communication viaone or more network access technologies, such as Global System forMobile communication (GSM), Universal Mobile Telecommunications System(UMTS), General Packet Radio Services (GPRS), Enhanced Data GSMEnvironment (EDGE), 3GPP Long Term Evolution (LTE), LTE Advanced,Wideband Code Division Multiple Access (WCDMA), Bluetooth, 802.11b/g/n,or the like. A wireless network may include virtually any type ofwireless communication mechanism by which signals may be communicatedbetween devices, such as a client device or a computing device, betweenor within a network, or the like.

Internet Protocol

Signal packets communicated via a network, such as a network ofparticipating digital communication networks, may be compatible with orcompliant with one or more protocols. Signaling formats or protocolsemployed may include, for example, TCP/IP, UDP, DECnet, NetBEUI, IPX,Appletalk, or the like. Versions of the Internet Protocol (IP) mayinclude IPv4 or IPv6.

The Internet refers to a decentralized global network of networks. TheInternet includes LANs, WANs, wireless networks, or long haul publicnetworks that, for example, allow signal packets to be communicatedbetween LANs. Signal packets may be communicated between devices of anetwork, such as, for example, to one or more sites employing a localnetwork address. A signal packet may, for example, be communicated overthe Internet from a user site via an access device coupled to theInternet. Likewise, a signal packet may be forwarded via network devicesto a target site coupled to the network via a network access device, forexample. A signal packet communicated via the Internet may, for example,be routed via a path of gateways, servers, etc. that may route thesignal packet in accordance with a target address and availability of anetwork path to the target address.

Network Architecture

The disclosed embodiments may be implemented in any of a wide variety ofcomputing contexts. FIG. 8 is a schematic diagram illustrating anexample embodiment of a network. Other embodiments that may vary, forexample, in terms of arrangement or in terms of type of components, arealso intended to be included within claimed subject matter.Implementations are contemplated in which users interact with a diversenetwork environment. As shown, FIG. 8, for example, includes a varietyof networks, such as a LAN/WAN 705 and wireless network 800, a varietyof devices, such as client devices 801-804, and a variety of serverssuch as content server(s) 807 and search server 806. The servers mayalso include an ad server (not shown). As shown in this example, theclient devices 801-804 may include one or more mobile devices 802, 803,804. Client device(s) 801-804 may be implemented, for example, via anytype of computer (e.g., desktop, laptop, tablet, etc.), media computingplatforms (e.g., cable and satellite set top boxes), handheld computingdevices (e.g., PDAs), cell phones, or any other type of computing orcommunication platform.

The disclosed embodiments may be implemented in some centralized manner.This is represented in FIG. 8 by server(s) 807, which may correspond tomultiple distributed devices and data store(s). The server(s) 807 and/orcorresponding data store(s) may store user account data, userinformation, and/or content.

Server

A computing device may be capable of sending or receiving signals, suchas via a wired or wireless network, or may be capable of processing orstoring signals, such as in memory as physical memory states, and may,therefore, operate as a server. Thus, devices capable of operating as aserver may include, as examples, dedicated rack-mounted servers, desktopcomputers, laptop computers, set top boxes, integrated devices combiningvarious features, such as two or more features of the foregoing devices,or the like.

Servers may vary widely in configuration or capabilities, but generallya server may include one or more central processing units and memory. Aserver may also include one or more mass storage devices, one or morepower supplies, one or more wired or wireless network interfaces, one ormore input/output interfaces, or one or more operating systems, such asWindows Server, Mac OS X, Unix, Linux, FreeBSD, or the like.

Content Server

A content server may comprise a device that includes a configuration toprovide content via a network to another device. A content server may,for example, host a site, such as a social networking site, examples ofwhich may include, without limitation, Flicker, Twitter, Facebook,LinkedIn, or a personal user site (such as a blog, vlog, online datingsite, etc.). A content server may also host a variety of other sites,including, but not limited to business sites, educational sites,dictionary sites, encyclopedia sites, wikis, financial sites, governmentsites, etc.

A content server may further provide a variety of services that include,but are not limited to, web services, third-party services, audioservices, video services, email services, instant messaging (IM)services, SMS services, MMS services, FTP services, voice over IP (VOIP)services, calendaring services, photo services, or the like. Examples ofcontent may include text, images, audio, video, or the like, which maybe processed in the form of physical signals, such as electricalsignals, for example, or may be stored in memory, as physical states,for example.

Examples of devices that may operate as a content server include desktopcomputers, multiprocessor systems, microprocessor-type or programmableconsumer electronics, etc.

Client Device

FIG. 9 is a schematic diagram illustrating an example embodiment of aclient device in which various embodiments may be implemented. A clientdevice may include a computing device capable of sending or receivingsignals, such as via a wired or a wireless network. A client device may,for example, include a desktop computer or a portable device, such as acellular telephone, a smart phone, a display pager, a radio frequency(RF) device, an infrared (IR) device, a Personal Digital Assistant(PDA), a handheld computer, a tablet computer, a laptop computer, a settop box, a wearable computer, an integrated device combining variousfeatures, such as features of the forgoing devices, or the like. Aportable device may also be referred to as a mobile device or handhelddevice.

As shown in this example, a client device 900 may include one or morecentral processing units (CPUs) 922, which may be coupled via connection924 to a power supply 926 and a memory 930. The memory 930 may includerandom access memory (RAM) 932 and read only memory (ROM) 934. The ROM934 may include a basic input/output system (BIOS) 940.

The RAM 932 may include an operating system 941. More particularly, aclient device may include or may execute a variety of operating systems,including a personal computer operating system, such as a Windows, iOSor Linux, or a mobile operating system, such as iOS, Android, or WindowsMobile, or the like. The client device 900 may also include or mayexecute a variety of possible applications 942 (shown in RAM 932), suchas a client software application such as messenger 943, enablingcommunication with other devices, such as communicating one or moremessages, such as via email, short message service (SMS), or multimediamessage service (MMS), including via a network, such as a socialnetwork, including, for example, Facebook, LinkedIn, Twitter, Flickr, orGoogle, to provide only a few possible examples. The client device 800may also include or execute an application to communicate content, suchas, for example, textual content, multimedia content, or the like, whichmay be stored in data storage 944. A client device may also include orexecute an application such as a browser 945 to perform a variety ofpossible tasks, such as browsing, searching, playing various forms ofcontent, including locally stored or streamed video, or games (such asfantasy sports leagues).

The client device 900 may send or receive signals via one or moreinterface(s). As shown in this example, the client device 900 mayinclude one or more network interfaces 950. The client device 900 mayinclude an audio interface 952. In addition, the client device 900 mayinclude a display 954 and an illuminator 958. The client device 900 mayfurther include an Input/Output interface 960, as well as a HapticInterface 962 supporting tactile feedback technology.

The client device 900 may vary in terms of capabilities or features.Claimed subject matter is intended to cover a wide range of potentialvariations. For example, a cell phone may include a keypad such 956 suchas a numeric keypad or a display of limited functionality, such as amonochrome liquid crystal display (LCD) for displaying text. Incontrast, however, as another example, a web-enabled client device mayinclude one or more physical or virtual keyboards, mass storage, one ormore accelerometers, one or more gyroscopes, global positioning system(GPS) 964 or other location identifying type capability, or a displaywith a high degree of functionality, such as a touch-sensitive color 2Dor 3D display, for example. The foregoing is provided to illustrate thatclaimed subject matter is intended to include a wide range of possiblefeatures or capabilities.

According to various embodiments, input may be obtained using a widevariety of techniques. For example, input for downloading or launchingan application may be obtained via a graphical user interface from auser's interaction with a local application such as a mobile applicationon a mobile device, web site or web-based application or service and maybe accomplished using any of a variety of well-known mechanisms forobtaining information from a user. However, it should be understood thatsuch methods of obtaining input from a user are merely examples and thatinput may be obtained in many other ways.

In some embodiments, an identity of the user (e.g., owner) of the clientdevice may be statically configured. Thus, the device may be keyed to anowner or multiple owners. In other embodiments, the device mayautomatically determine the identity of the user of the device. Forinstance, a user of the device may be identified by deoxyribonucleicacid (DNA), retina scan, and/or finger print. From the identity of theuser, a user profile and/or client profile may be identified orobtained.

FIG. 10 illustrates a typical computer system that, when appropriatelyconfigured or designed, can serve as a system via which variousembodiments may be implemented. The computer system 1200 includes anynumber of CPUs 1202 that are coupled to storage devices includingprimary storage 1206 (typically a RAM), primary storage 1204 (typicallya ROM). CPU 1202 may be of various types including microcontrollers andmicroprocessors such as programmable devices (e.g., CPLDs and FPGAs) andunprogrammable devices such as gate array ASICs or general purposemicroprocessors. As is well known in the art, primary storage 1204 actsto transfer data and instructions uni-directionally to the CPU andprimary storage 1206 is used typically to transfer data and instructionsin a bi-directional manner. Both of these primary storage devices mayinclude any suitable computer-readable media such as those describedabove. A mass storage device 1208 is also coupled bi-directionally toCPU 1202 and provides additional data storage capacity and may includeany of the computer-readable media described above. Mass storage device1208 may be used to store programs, data and the like and is typically asecondary storage medium such as a hard disk. It will be appreciatedthat the information retained within the mass storage device 1208, may,in appropriate cases, be incorporated in standard fashion as part ofprimary storage 1206 as virtual memory. A specific mass storage devicesuch as a CD-ROM 1214 may also pass data uni-directionally to the CPU.

CPU 1202 may also be coupled to an interface 1210 that connects to oneor more input/output devices such as such as video monitors, trackballs, mice, keyboards, microphones, touch-sensitive displays,transducer card readers, magnetic or paper tape readers, tablets,styluses, voice or handwriting recognizers, or other well-known inputdevices such as, of course, other computers. Finally, CPU 1202optionally may be coupled to an external device such as a database or acomputer or telecommunications network using an external connection asshown generally at 1212. With such a connection, it is contemplated thatthe CPU might receive information from the network, or might outputinformation to the network in the course of performing the method stepsdescribed herein.

Regardless of the system's configuration, it may employ one or morememories or memory modules configured to store data, programinstructions for the general-purpose processing operations and/or theinventive techniques described herein. The program instructions maycontrol the operation of an operating system and/or one or moreapplications, for example. The memory or memories may also be configuredto store instructions for performing the disclosed methods, graphicaluser interfaces to be displayed in association with the disclosedmethods, etc.

Because such information and program instructions may be employed toimplement the systems/methods described herein, the disclosedembodiments relate to machine readable media that include programinstructions, state information, etc. for performing various operationsdescribed herein. Examples of machine-readable media include, but arenot limited to, magnetic media such as hard disks, floppy disks, andmagnetic tape; optical media such as CD-ROM disks; magneto-optical mediasuch as optical disks; and hardware devices that are speciallyconfigured to store and perform program instructions, such as ROM andRAM. Examples of program instructions include both machine code, such asproduced by a compiler, and files containing higher level code that maybe executed by the computer using an interpreter.

Computer program instructions with which various embodiments areimplemented may be stored in any type of computer-readable media, andmay be executed according to a variety of computing models including aclient/server model, a peer-to-peer model, on a stand-alone computingdevice, or according to a distributed computing model in which variousof the functionalities described herein may be effected or employed atdifferent locations.

The disclosed techniques may be implemented in any suitable combinationof software and/or hardware system, such as a web-based server ordesktop computer system. Moreover, a system implementing variousembodiments may be a portable device, such as a laptop or cell phone. Anapparatus and/or web browser may be specially constructed for therequired purposes, or it may be a general-purpose computer selectivelyactivated or reconfigured by a computer program and/or data structurestored in the computer. The processes presented herein are notinherently related to any particular computer or other apparatus. Inparticular, various general-purpose machines may be used with programswritten in accordance with the teachings herein, or it may be moreconvenient to construct a more specialized apparatus to perform thedisclosed method steps.

Although the foregoing invention has been described in some detail forpurposes of clarity of understanding, it will be apparent that certainchanges and modifications may be practiced within the scope of theappended claims. Therefore, the present embodiments are to be consideredas illustrative and not restrictive and the invention is not to belimited to the details given herein, but may be modified within thescope and equivalents of the appended claims.

What is claimed is:
 1. A method, comprising: ascertaining a currentcontext of a mobile device, the current context including an indicationof an action performed via the mobile device, the action being performedin relation to one of a plurality of applications installed on themobile device; determining a probability, for each of the plurality ofapplications, that a user of the mobile device will use thecorresponding application under the current context, wherein theprobability for at least a portion of the plurality of applications isdetermined by applying a computer-generated model to the currentcontext; and identifying one or more of the plurality of theapplications based, at least in part, upon the probability, for each oneof the plurality of applications, that the user of the mobile devicewill use the corresponding application.
 2. The method as recited inclaim 1, further comprising: recommending the identified one or more ofthe plurality of applications, launching the identified one or more ofthe plurality of applications, or providing content pertaining to theidentified one or more of the plurality of applications.
 3. The methodas recited in claim 1, wherein the current context comprises a valueassociated with one or more of a Last Application Opened, a LastLocation Update, Last Charge Cable, Last Audio Cable, Last ContextTrigger, or a Last Context Pulled.
 4. The method as recited in claim 1,further comprising: collecting contextual information via the mobiledevice, the contextual information including a context pertaining to oneor more actions detected via the mobile device, wherein at least one ofthe one or more actions pertains to usage of the at least a portion ofthe plurality of applications installed on the mobile device; andtraining the computer-generated model using at least a portion of thecollected contextual information.
 5. The method as recited in claim 4,wherein the one or more actions comprise at least one of an applicationopen action, a location update action, a charge cable action, an audiocable action, a context trigger action, or a context pulled action. 6.The method as recited in claim 4, wherein the context indicates asequential pattern of actions detected by the mobile device prior to oneof the one or more actions and/or after the one of the one or moreactions.
 7. The method as recited in claim 1, further comprising:determining that a particular one of the plurality of applications is anewly installed application such that a threshold amount of contextualinformation pertaining to usage of the newly installed application onthe mobile device cannot be ascertained; and determining whether thenewly installed application is a short-term application or a long-termapplication; wherein determining a probability, for each of theplurality of applications, that a user of the mobile device will use thecorresponding application includes ascertaining a probability that auser of the mobile device will use the newly installed applicationbased, at least in part, upon whether the newly installed application isa short-term application or a long-term application.
 8. The method asrecited in claim 1, further comprising: determining that a particularone of the plurality of applications is a newly installed applicationsuch that a threshold amount of contextual information pertaining tousage of the newly installed application on the mobile device cannot beascertained; and determining that the newly installed application is ashort-term application; wherein determining a probability, for each ofthe plurality of applications, that a user of the mobile device will usethe corresponding application includes ascertaining a probability that auser of the mobile device will use the newly installed applicationbased, at least in part, upon historical data pertaining to a pluralityof other mobile devices with respect to the particular one of theplurality of applications.
 9. The method as recited in claim 1, furthercomprising: determining that a particular one of the plurality ofapplications is a newly installed application such that a thresholdamount of contextual information pertaining to usage of the newlyinstalled application on the mobile device cannot be ascertained; anddetermining that the newly installed application is a long-termapplication; wherein determining a probability, for each of theplurality of applications, that a user of the mobile device will use thecorresponding application includes ascertaining a probability that auser of the mobile device will use the newly installed applicationbased, at least in part, upon historical data, the historical datapertaining to use of the plurality of applications on the mobile deviceand use of the particular application on other mobile devices.
 10. Anapparatus, comprising: a processor; and a memory storing thereoncomputer-readable instructions, the computer-readable instructions beingconfigured to: ascertain whether a threshold amount of contextualinformation pertaining to usage of at least a portion of a plurality ofapplications installed on a mobile device is available, the contextualinformation including a context pertaining to one or more actionsdetected via the mobile device; determine a probability, for each of theplurality of applications, that a user of the mobile device will use thecorresponding application under a current context, based, at least inpart, upon whether a threshold amount of contextual informationpertaining to usage of at least a portion of the plurality ofapplications installed on the mobile device is available; and identifyone or more of the plurality of the applications based, at least inpart, upon the ascertained probability, for each one of the plurality ofapplications, that the user of the mobile device will use thecorresponding application.
 11. The apparatus as recited in claim 10,wherein ascertaining whether a threshold amount of contextualinformation pertaining to usage of at least a portion of a plurality ofapplications installed on a mobile device is available comprisesascertaining that a threshold amount of contextual informationpertaining to usage of at least a portion of the plurality ofapplications installed on the mobile device is not available, andwherein determining a probability, for each of the plurality ofapplications, that a user of the mobile device will use thecorresponding application, based, at least in part, upon whether athreshold amount of contextual information pertaining to usage of atleast a portion of the plurality of applications installed on the mobiledevice is available comprises: obtaining information associated with atleast one other mobile device having applications installed thereon thatinclude at least a portion of the plurality of applications; anddetermining a probability, for each of the plurality of applications,based, at least in part, upon the information.
 12. The apparatus asrecited in claim 10, wherein the one or more actions comprise at leastone of an application open action, a location update action, a chargecable action, an audio cable action, a context trigger action, or acontext pulled action.
 13. The apparatus as recited in claim 10, whereinthe current context comprises one or more of a Last Application Opened,a Last Location Update, Last Charge Cable, Last Audio Cable, LastContext Trigger, Last Context Pulled, or a Last Application Open foreach application installed on the mobile device.
 14. The apparatus asrecited in claim 10, the computer-readable instructions being furtherconfigured to: determine that a particular one of the plurality ofapplications is a newly installed application such that contextualinformation pertaining to usage of the newly installed application onthe mobile device cannot be ascertained or is limited; and determinewhether the newly installed application is a short-term application or along-term application; wherein determining a probability, for each ofthe plurality of applications, that a user of the mobile device will usethe corresponding application includes ascertaining a probability that auser of the mobile device will use the newly installed applicationbased, at least in part, upon whether the newly installed application isa short-term application or a long-term application.
 15. The apparatusas recited in claim 10, the computer-readable instructions being furtherconfigured to: determine that a particular one of the plurality ofapplications is a newly installed application such that contextualinformation pertaining to usage of the newly installed application onthe mobile device cannot be ascertained or is limited; and determinethat the newly installed application is a short-term application;wherein determining a probability, for each of the plurality ofapplications, that a user of the mobile device will use thecorresponding application includes ascertaining a probability that auser of the mobile device will use the newly installed applicationbased, at least in part, upon historical data pertaining to a pluralityof other mobile devices with respect to the particular one of theplurality of applications.
 16. The apparatus as recited in claim 10, thecomputer-readable instructions being configured to: determine that aparticular one of the plurality of applications is a newly installedapplication such that contextual information pertaining to usage of thenewly installed application on the mobile device cannot be ascertainedor is limited; and determine that the newly installed application is along-term application; wherein determining a probability, for each ofthe plurality of applications, that a user of the mobile device will usethe corresponding application includes ascertaining a probability that auser of the mobile device will use the newly installed applicationbased, at least in part, upon historical data, wherein the historicaldata pertains to usage of the plurality of applications on the mobiledevice and usage of the particular application on other mobile devices.17. The apparatus as recited in claim 10, the computer-readableinstructions being configured to: recommend the identified one or moreof the plurality of applications or providing content pertaining to theidentified one or more of the plurality of applications.
 18. Anon-transitory computer-readable storage medium storing thereoncomputer-readable instructions configured to: ascertain a currentcontext of a mobile device, the current context including an indicationof an action detected via the mobile device; determine a probability,for each of a plurality of applications installed on the mobile device,that a user of the mobile device will use the corresponding applicationunder the current context, wherein the probability for at least aportion of the plurality of applications is determined by applying acomputer-generated model to the current context; and identify one ormore of the plurality of the applications based, at least in part, uponthe probability, for each one of the plurality of applications, that theuser of the mobile device will use the corresponding application. 19.The non-transitory computer-readable storage medium as recited in claim18, the computer-readable instructions being further configured to:recommend the identified one or more of the plurality of applications,launch the identified one or more of the plurality of applications, orprovide content pertaining to the identified one or more of theplurality of applications.
 20. The non-transitory computer-readablestorage medium as recited in claim 18, wherein the current contextcomprises a value associated with one or more of a Last ApplicationOpened, Last Location Update, Last Charge Cable, Last Audio Cable, LastContext Trigger, or Last Context Pulled.